The development of
polymers that can replace engineered viral vectors
in clinical gene therapy has proven elusive despite the vast portfolios
of multifunctional polymers generated by advances in polymer synthesis.
Functional delivery of payloads such as plasmids (pDNA) and ribonucleoproteins
(RNP) to various cellular populations and tissue types requires design
precision. Herein, we systematically screen a combinatorially designed
library of 43 well-defined polymers, ultimately identifying a lead
polycationic vehicle (P38) for efficient pDNA delivery. Further, we
demonstrate the versatility of P38 in codelivering spCas9 RNP and
pDNA payloads to mediate homology-directed repair as well as in facilitating
efficient pDNA delivery in ARPE-19 cells. P38 achieves nuclear import
of pDNA and eludes lysosomal processing far more effectively than
a structural analogue that does not deliver pDNA as efficiently. To
reveal the physicochemical drivers of P38’s gene delivery performance,
SHapley Additive exPlanations (SHAP) are computed for nine polyplex
features, and a causal model is applied to evaluate the average treatment
effect of the most important features selected by SHAP. Our machine
learning interpretability and causal inference approach derives structure–function
relationships underlying delivery efficiency, polyplex uptake, and
cellular viability and probes the overlap in polymer design criteria
between RNP and pDNA payloads. Together, combinatorial polymer synthesis,
parallelized biological screening, and machine learning establish
that pDNA delivery demands careful tuning of polycation protonation
equilibria while RNP payloads are delivered most efficaciously by
polymers that deprotonate cooperatively
via
hydrophobic
interactions. These payload-specific design guidelines will inform
further design of bespoke polymers for specific therapeutic contexts.